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自动图像分析中学习人脸外观的挑战第1部分
Challenges in Learning the Appearance of Faces for Automated Image Analysis - Part 1 |
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| 课程网址: |
http://videolectures.net/lmcv04_verri_clafa1/
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| 主讲教师: |
Alessandro Verri
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| 开课单位: |
热那亚大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
人脸图像的可变性从一开始就挑战了机器视觉的研究。可变性的来源不仅包括个体外观，还包括外部参数，例如影响图像形成过程的透视和照明。人脸图像分析研究目前分为人脸检测和人脸识别方向。尽管共同的目标是补偿图像中面部的大变化，但这两个方面的方法和问题设置仍然存在很大差异。在这两个领域中，机器学习策略用于从示例面部学习人脸外观的一般模型。在我们演示的第一部分，我们想回顾一下人脸检测和人脸识别方面的最新技术。在第二部分中，我们将比较所使用的不同策略，并尝试描述一般图像模型的要求，该模型可以作为检测和识别研究的基础。对于面部检测，我们将关注基于对面部示例计算的大量特征的概率分布的估计的方法。在选择看起来与任务最相关的特征子集之后，通过组合适当定义的统计测试的结果来检测面部。与基于正面和负面示例的现有技术相比，该方法仅基于正例，似乎给出了非常有希望的结果。在人脸识别中，我们将专注于使用合成框架分析的方法，例如可变形模型，活动外观或形状模型。目前，这些方法似乎是能够解释透视和照明变化的最有希望的方法。
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| 课程简介: |
The variability of images of the human face challenges research in machine vision since its beginning. Sources of variability not only include individual appearance but also cover external parameters such as perspective and illumination that influence the image formation process heavily. Research on the analysis of face images currently splits into the directions of face detection and face recognition. Approaches and problem setting in this two areas are still quite different despite the common goal to compensate for the large variability of faces in images. In both areas machine learning strategies are used to learn from example faces a general model of the appearance of human faces. In the first part of our presentation we would like to review the current state of the art in face detection and in face recognition. In a second part we will compare the different strategies used and try to describe the requirements of a general image model that could serve as basis in detection as well as in recognition research. For face detection we will focus on methods based on the estimation of the probability distribution of large number of features computed on face examples. After selecting the subset of features which appear to be most relevant to the task, faces are detected by combining the outcome of suitably defined statistical tests. This method, which is based on positive examples only, seems to give very promising results compared to state of the art techniques based on both positive and negative examples. In face recognition we will concentrate on methods that use an analysis by synthesis framework such as morphable models, active appearance or shape models. Currently these approaches seem the most promising methods able to account for variations in perspective and illumination.
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| 关 键 词: |
机器视觉; 机器学习; 人脸识别
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-05-14：lxf
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| 阅读次数: |
64
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